A comprehensible analysis of the efficacy of Ensemble Models for Bug Prediction

• URL: http://arxiv.org/abs/2310.12133v1
• Date: Wed, 18 Oct 2023 17:43:54 GMT
• Title: A comprehensible analysis of the efficacy of Ensemble Models for Bug Prediction
• Authors: Ingrid Mar\c{c}al and Rog\'erio Eduardo Garcia
• Abstract summary: We present a comparison and analysis of the efficacy of two AI-based approaches, namely single AI models and ensemble AI models, for predicting the probability of a Java class being buggy. Our experimental findings indicate that the ensemble of AI models can outperform the results of applying individual AI models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The correctness of software systems is vital for their effective operation. It makes discovering and fixing software bugs an important development task. The increasing use of Artificial Intelligence (AI) techniques in Software Engineering led to the development of a number of techniques that can assist software developers in identifying potential bugs in code. In this paper, we present a comprehensible comparison and analysis of the efficacy of two AI-based approaches, namely single AI models and ensemble AI models, for predicting the probability of a Java class being buggy. We used two open-source Apache Commons Project's Java components for training and evaluating the models. Our experimental findings indicate that the ensemble of AI models can outperform the results of applying individual AI models. We also offer insight into the factors that contribute to the enhanced performance of the ensemble AI model. The presented results demonstrate the potential of using ensemble AI models to enhance bug prediction results, which could ultimately result in more reliable software systems.

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